1. Field of the Invention
This invention is related in general to methods for analyzing biological samples. In particular, it relates to a method for a quantitative prediction of a cancer event recurrence based on karyometric assessment of nuclei from a tissue sample.
2. Description of the Related Art
Changes in the cellular structure of tissue are used to detect pathologic changes, to assess the progress of precancerous conditions, and to detect cancer. A tissue sample removed from a patient is typically sectioned and fixed to a slide for staining and microscopic examination by a pathologist. The morphology of the tissue (the visually perceptible structure and shape of components in the tissue) is analyzed to provide a qualitative assessment of its condition and to identify the presence of pathologic changes, such as may indicate progression towards a malignancy. For many decades, this visual procedure has been the diagnostic mainstay of pathology.
With the advent of computers and sophisticated digital imaging equipment, researchers have extended the realm of histopathology through the use of mechanized procedures for diagnostic and quantitative investigation. For example, U.S. Pat. No. 6,204,064 describes a method for measuring quantitatively the progression of a lesion toward malignancy by digitizing the images of clinical samples and analyzing nuclear chromatin texture features in the nuclei captured in the images. Numerical values are assigned to these features and compared to a monotonic progression curve previously established using the same criteria on known clinical samples ranging from normal to malignant tissue. Thus, the procedure provides a quantitative assessment of the condition of the tissue as well as a method for testing the efficacy of chemopreventive drugs or therapeutic treatments.
In such mechanized procedures, histopathologic sections and/or cytologic preparations are imaged with a microscope, and the images are digitized, stored, and analyzed for nuclear-placement patterns (histometry) or for the spatial and statistical distribution patterns of nuclear chromatin (karyometry). Karyometric assessment is always preceded by image segmentation, whereby each nucleus in an image is identified, outlined, isolated and stored as a separate image. As those skilled in the art readily understand, the nuclear chromatin pattern is an artifact of tissue fixation, but its spatial and statistical distributions are highly reproducible measures of the metabolic and functional state of cells. Thus, nuclear chromatin patterns have always been used in pathology to provide diagnostic clues. For example, the state of differentiation of the nucleus and its metabolic function may be reliably assessed based on a finding that nuclear chromatin is finely dispersed, coarsely aggregated, granular, clumped, or displaced toward the nuclear periphery.
Many chromatin texture features derived from the optical density of the tissue image have been identified as statistically significant for diagnostic purposes. Accordingly, after a sample is imaged and the image is digitized to provide an optical density value for each image pixel, the information is used in conventional manner first to identify and isolate each nucleus within the sample (image segmentation), and then to analyze chromatin patterns within each nucleus. The optical density recorded for each pixel is used to characterize chromatin features with statistical significance as characteristics for identifying changes in the condition of the tissue. These features are then used much as the statistics of alphabet letters can be used to identify features of a written text that are not readily perceptible by visual inspection. For example, the proportion of each letter appearing in a text, or the frequency of occurrence of certain letter digrams or trigrams, can be used to identify the language even though the text is not understood. Similarly, the spatial and statistical distribution of optical density in a nucleus can be used to detect chromatin patterns that are not visually perceptible. This notion has provided a useful vehicle for achieving advantageous refinements in the detection of pathological change and of precancerous and cancerous lesions.
Optical density (OD) of a sample is defined in the art as the logarithm of the ratio of the light incident to the sample and the light transmitted through it. As used in microscopic imagery, optical density is usually expressed in terms of base-ten logarithmic values that range between zero and about 1.80 (because the accuracy of measurement limits near-zero transmission readings). An OD value of zero refers to full transmission, while an OD of 1.80 refers to transmission slightly greater than 1 percent. OD values are conventionally grouped into intervals of 0.10 OD units. For convenience, OD values may be multiplied by a factor of 100, so that computations can be carried out with integers (for example, an OD value of 1.0 is represented by 100, which corresponds to 10% light transmission).
As mentioned above, many features may be defined from the statistical and spatial distribution of nuclear chromatin. Global features are computed from the nucleus as a whole. For example, “total optical density” is defined as the sum of all pixel OD values in the nuclear area (i.e., the number of pixels within the outline of a nucleus). This feature is known to be related to the DNA ploidy of the nucleus, a measure of genetic instability and a diagnostic clue for progression toward a pre-malignant or malignant lesion. The variance of optical density within a nucleus is another example of global feature. Other features are local in nature, such as the frequency of occurrence of particular OD values within a certain interval, and have been identified in the art as indicative of tissue condition.
According to related-art procedures, the chromatin features characterized using pixel OD values as described above have been reduced to number values representative of a quantitative measure of each feature and of a chromatin or nuclear “signature” representative of a set of features. These numeric values have then been used to provide pathologists with quantitative information available to complement their visual evaluation of tissue slides. For example, as generally described in U.S. Pat. No. 6,204,064, the information derived from the nuclear signature can be used advantageously as a quantitative measure of progression toward a lesion. That is, the physician is provided with information representative of a result formulated by the analytical algorithm built into the diagnostic system (e.g., a numerical value assigned to the nuclear signature calculated by the system and a resulting position on a progression curve).
While the related-art use of digitized karyometry techniques has been very useful in diagnosis and in charting progression from a preneoplastic to a malignant state, providing a quantitative prognosis for recurrence of a cancer event after a primary tumor has be treated or removed based on karyometric analysis only recently has been attempted. These analysis were performed by linear discriminant analysis and resulted in having a statistically significant difference between non-recurrent and recurrent cases of superficial transitional bladder carcinoma (see Van Velthoven et al., The Journal of Urology, Vol. 164, 2134-2137 [December 2000]). However, the correct prediction rates for recurrence in all cases did not reach clinically useful levels. Also, in the past, a variety of immunohistochemical and molecular markers have been applied to predict disease occurrence and recurrence. However, results from studies utilizing these markers have been inconclusive at best.
Therefore, there is still a need for a mechanized diagnostic system that provides a measure of a chromatin feature(s) that is a statistically significant indicator of a cancer event recurrence (e.g., such an event might include the recurrence of a pre-malignant or malignant lesion). Such a method would enable a pathologist to consider karyometric features that are not visually detectable yet correlate with future cancer-related disease events, thus allowing a prediction of whether recurrence of a specific disease event can be expected. Accordingly, monitoring, chemoprevention and intervention can be appropriately gauged and implemented.
At this time, there are over 10 million cancer survivors in the United States alone. A substantial proportion of these survivors must expect a recurrence of their primary tumor within a few year's time. A method allowing the identification of those patients for whom recurrence is likely would be an invaluable medical tool in efforts to increase the quality and longevity of cancer survivor's lives.